scholarly journals A Novel Integrated Type 2 Diabetes Prediction Model for Indian Population using Data Mining Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 3067-3072
Keyword(s):  
Type 2 Diabetes ◽  
Prediction Model ◽  
Risk Prediction ◽  
Indian Population ◽  
Integrated Model ◽  
Diabetes Risk ◽  
Classification Model ◽  
Diabetes Prediction ◽  
Diabetes Risk Score

Late diagnosis and undiagnosed type 2 diabetes are the two major concerns for India, which is going to be a diabetes capital shortly. Several diabetes risk score (DRS) tools have been proposed and deployed for detecting the persons with high risk. These DRS tools have been developed using the multiple logistic regression model. But this model is both imperfect and subject to misuse. Another major issue with the DRS tools developed for Indian population is that they are based on the very limited urban population that does not represent the population of India. The objective of current research work is to develop a classification model for type 2 diabetes prediction. Along with this, the building of a novel integrated model for type 2 diabetes risk prediction is discussed consisting of the aggregate classification model and Indian weighted diabetes risk score model. The dataset used to develop and validate the model is obtained from the Annual Health Survey comprising of nearly 0.7 million and nearly 75 thousand adult participants respectively from around 400 districts of India. The proposed integrated diabetes risk prediction model predicts diabetes with 69.89% sensitivity, 56.58% specificity. The positive predictive value of the proposed integrated model is 15.88%, which is a significant improvement as the prevalence of diabetes is only 3.68% for the study population. Developing countries such as India, where undiagnosed diabetes and limited financial resources are a significant concern, the proposed integrated model for diabetes risk prediction can be useful as a cheaper tool useful for mass-screening, which can save up to 30% of the total screening cost.

scholarly journals Validity of the Finnish Diabetes Risk Score for Detecting Undiagnosed Type 2 Diabetes among General Medical Outpatients in Botswana

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Bernard Omech ◽  
Julius Chacha Mwita ◽  
Jose-Gaby Tshikuka ◽  
Billy Tsima ◽  
Oathokwa Nkomazna ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Predictive Value ◽  
Total Population ◽  
Diabetes Risk ◽  
Undiagnosed Diabetes ◽  
Haemoglobin A1c ◽  
Diabetes Risk Score ◽  
Medical Outpatients

This was a cross-sectional study designed to assess the validity of the Finnish Diabetes Risk Score for detecting undiagnosed type 2 diabetes among general medical outpatients in Botswana. Participants aged ≥20 years without previously diagnosed diabetes were screened by (1) an 8-item Finnish diabetes risk assessment questionnaire and (2) Haemoglobin A1c test. Data from 291 participants were analyzed (74.2% were females). The mean age of the participants was 50.1 (SD = ±11) years, and the prevalence of undiagnosed diabetes was 42 (14.4%) with no significant differences between the gender (20% versus 12.5%,P=0.26). The area under curve for detecting undiagnosed diabetes was 0.63 (95% CI 0.55–0.72) for the total population, 0.65 (95% CI: 0.56–0.75) for women, and 0.67 (95% CI: 0.52–0.83) for men. The optimal cut-off point for detecting undiagnosed diabetes was 17 (sensitivity = 48% and specificity = 73%) for the total population, 17 (sensitivity = 56% and specificity = 66%) for females, and 13 (sensitivity = 53% and specificity = 77%) for males. The positive predictive value and negative predictive value were 20% and 89.5%, respectively. The findings indicate that the Finnish questionnaire was only modestly effective in predicting undiagnosed diabetes among outpatients in Botswana.

scholarly journals A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus

2019 ◽  
Vol 43 (3) ◽  
pp. 275-283 ◽  
Author(s):  
Brent A. Williams ◽  
Daniela Geba ◽  
Jeanine M. Cordova ◽  
Sharash S. Shetty
Keyword(s):  
Diabetes Mellitus ◽  
Heart Failure ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Heart Failure Hospitalization

scholarly journals Genetic risk score for risk prediction of diabetic nephropathy in Han Chinese type 2 diabetes patients

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Li-Na Liao ◽  
Tsai-Chung Li ◽  
Chia-Ing Li ◽  
Chiu-Shong Liu ◽  
Wen-Yuan Lin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Risk Score ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Conventional Risk Factors ◽  
Diabetes Patients

AbstractWe evaluated whether genetic information could offer improvement on risk prediction of diabetic nephropathy (DN) while adding susceptibility variants into a risk prediction model with conventional risk factors in Han Chinese type 2 diabetes patients. A total of 995 (including 246 DN cases) and 519 (including 179 DN cases) type 2 diabetes patients were included in derivation and validation sets, respectively. A genetic risk score (GRS) was constructed with DN susceptibility variants based on findings of our previous genome-wide association study. In derivation set, areas under the receiver operating characteristics (AUROC) curve (95% CI) for model with clinical risk factors only, model with GRS only, and model with clinical risk factors and GRS were 0.75 (0.72–0.78), 0.64 (0.60–0.68), and 0.78 (0.75–0.81), respectively. In external validation sample, AUROC for model combining conventional risk factors and GRS was 0.70 (0.65–0.74). Additionally, the net reclassification improvement was 9.98% (P = 0.001) when the GRS was added to the prediction model of a set of clinical risk factors. This prediction model enabled us to confirm the importance of GRS combined with clinical factors in predicting the risk of DN and enhanced identification of high-risk individuals for appropriate management of DN for intervention.

scholarly journals The Efficacy of New Chinese Diabetes Risk Score in Screening Undiagnosed Type 2 Diabetes and Prediabetes: A Community-Based Cross-Sectional Study in Eastern China

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Tao Mao ◽  
Jiayan Chen ◽  
Haijian Guo ◽  
Chen Qu ◽  
Chu He ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Eastern China ◽  
Cross Sectional Study ◽  
Diabetes Risk ◽  
Undiagnosed Diabetes ◽  
Cross Sectional ◽  
Community Residents ◽  
Diabetes Risk Score

The New Chinese Diabetes Risk Score (NCDRS) is one of the recommended tools for screening undiagnosed type 2 diabetes in China. However, its performance in detecting undiagnosed diabetes needs to be verified in different community populations. Also, it is unknown whether NCDRS can be used in detecting prediabetes. In the present study, we aimed to evaluate the performance of NCDRS in detecting undiagnosed diabetes and prediabetes among the community residents in eastern China. We applied NCDRS in 7675 community residents aged 18-65 years old in Jiangsu Province. The results showed that the participants with undiagnosed diabetes reported the highest NCDRS value, followed by those with prediabetes (P<0.001). The best cut-off points of NCDRS for detecting undiagnosed diabetes and prediabetes were 27 (with a sensitivity of 78.0% and a specificity of 57.7%) and 27 (with a sensitivity of 66.0% and a specificity of 62.9%). The AUCs of NCDRS for identifying undiagnosed diabetes and prediabetes were 0.749 (95% CI: 0.739~0.759) and 0.694 (95% CI: 0.683~0.705). These results demonstrate the excellent performance of NCDRS in screening undiagnosed diabetes in the community population in eastern China and further provide evidence for using NCDRS in detecting prediabetes.

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